Building Fair Pay: A Guide to Compensation Benchmarking
Learn how organizations build accurate, defensible pay benchmarks using market data, medians, and job matching to ensure fair and competitive compensation.
Understanding Our Market Data
Building Confidence in Pay Benchmarking
How We Ensure Our Pay Data is Accurate, Current & Defensible
Reward & Pay Strategy
What We Keep Hearing...
I got a 30% pay rise when I left!
Company X pays way more for the same role
Our market data must be outdated
Someone told me the market has moved significantly
1 person's experience ≠ market reality. Our data covers 600+ organisations and thousands of data points.
Reward & Pay Strategy
Our Market Data Approach<br/><br/><br/><br/><br/><br/>
Market Competitiveness & Fairness
Consistency Across Grades & Geographies<br/>
Pay Transparency & Governance
Data Submission & Collection
Market Data Validation & Job Matching
Statistical Analysis & Adjustments
Governance & Quality Assurance
Final Pay Range Construction
600+
Organisations
All
Big 4 Firms
500+
Consulting Firms
30+
Legal Firms
How We Benchmark Pay
Our 4-Step Approach to Fair, Accurate Pay Positioning
01
How Are Roles Matched?
Job Grouping + Pay Grade + Service Type + Geozone = Fair Like-for-Like Comparison
02
Job Matching & Data Validation
Match to survey codes → Validate sample size (min 5 orgs) → Adjust granularity if needed
03
How We Calculate Market Median
Raw median → Age forward → Blend across surveys = Smoothes=Single balanced reference point
04
Final Pay Construction
Median = Pay Range Midpoint → Leadership sets percentile targets based on affordability → Market-aligned & equitable
WE USE WTW,MCLAGAN,MERCER ETC. — THE WORLD'S LEADING INDEPENDENT SALARY SURVEY PROVIDERS.
Why the Median Protects Against Noise
Statistically Resilient
Median acts as a dampener. A few extreme outliers easily skew an average, but have near zero impact on a median.
Aged Forward
Data is reliably aged forward annually, ensuring all numbers accurately reflect current, up-to-date pay timing.
Balanced Reference
Blended securely across multiple reputable surveys to provide a single, balanced, and defensible source of truth.
One employee's pay offer is 1 data point. Our median is built on thousands
Busting the Myths
Addressing Common Misconceptions
❌ The Myth
✅ The Reality
Reward can check what Company X pays
Not legally possible — data is anonymised & aggregated across hundreds of firms
We can pick our own peer group
Peer groups are set by data providers — we pay for a custom study, not cherry-picked
Job titles should match directly
Titles vary widely — we use standardised job codes for accurate like-for-like matching
Market data shows live pay
Data is annual and aged forward — it reflects current market timing
Missing companies break the data
Medians rely on large datasets — 2-3 missing firms have zero meaningful impact
The Risk of Ignoring Benchmarked Data
With Benchmarked Data
Without Benchmarked Data
Objective, defensible pay decisions
Consistent pay across grades
Evidence-based reward strategy
Protection against pay drift
Leadership confidence in pay
Pay decisions based on rumour & anecdote
Reactive, inconsistent outcomes
Increased pay volatility & bias
Loss of competitive market insight
Reduced leadership confidence
Anecdotal evidence creates noise. Benchmarked data creates strategy.
From Data to Pay Range:
Our Process
External Survey Data
WTW, Mercer, Radford, Vencon — 600+ orgs
Job Matching
Match EY roles to survey codes using Job Group + Grade + Service + Geozone
Market Median Calculated
50th percentile — aged forward, blended across surveys
Pay Range Tool
Median becomes the indicative midpoint. Width & final percentiles set by Reward based on SL affordability and agreed with SL leadership
Final Pay Range
Market-aligned, equitable, defensible pay structure
Every pay range starts with independently validated market data — not guesswork.
Our Market Data: Robust, Current & Defensible
Independent & Validated
Data from WTW & Mercer across 600+ organisations — not internal estimates
Statistically Robust
Medians are resilient to outliers. Missing 2–3 firms has zero impact on the result
Annually Updated
Data aged forward each cycle to reflect current pay market timing
We benchmark to the market — not to the loudest voice in the room.
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- hr-strategy
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